Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Dimitrios Tsoumakos"'
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 7, Iss 1, Pp 1-20 (2018)
Abstract Application deployment is a crucial operation for modern cloud providers. The ability to dynamically allocate resources and deploy a new application instance based on a user-provided description in a fully automated manner is of great import
Externí odkaz:
https://doaj.org/article/2620880994794002aae62f5aa25799c4
Publikováno v:
Distributed and Parallel Databases. 39:445-482
In this work, we study the problem of maintaining basic aggregate statistics over a sliding-window data stream under the constraint of limited memory. As in IoT scenarios the available memory is typically much less than the window size, queries are a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::de70cb9c2d44fd377e7b5ceb942444f0
https://doi.org/10.1007/s10619-020-07307-w
https://doi.org/10.1007/s10619-020-07307-w
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering. 31:1794-1808
Modern analytics involve computations over enormous numbers of data records. The volume of data and the stringent response-time requirements place increasing emphasis on the efficiency of approximate query processing. A major challenge over the past
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::57bc4e673afbf2a5be06aaa8fb0815f1
https://doi.org/10.1109/tkde.2018.2867185
https://doi.org/10.1109/tkde.2018.2867185
Publikováno v:
SIGMOD Conference
Proceedings of the 2019 International Conference on Management of Data -SIGMOD '19
Proceedings of the 2019 International Conference on Management of Data-SIGMOD 19
Proceedings of the 2019 International Conference on Management of Data -SIGMOD '19
Proceedings of the 2019 International Conference on Management of Data-SIGMOD 19
The rapidly increasing amount of available data has created invaluable business opportunities but also new challenges. The focus on content-driven analytics is shifting attention from optimizing operators and systems to handle massive data sizes, to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f2d08b0e8ac686995df98adb90b2915c
https://doi.org/10.1145/3299869.3320220
https://doi.org/10.1145/3299869.3320220
Publikováno v:
Proceedings of the 31st International Conference on Scientific and Statistical Database Management-SSDBM 19
Proceedings of the 31st International Conference on Scientific and Statistical Database Management -SSDBM '19
Proceedings of the 31st International Conference on Scientific and Statistical Database Management
SSDBM
Proceedings of the 31st International Conference on Scientific and Statistical Database Management -SSDBM '19
Proceedings of the 31st International Conference on Scientific and Statistical Database Management
SSDBM
The IoT era has brought forth a computing paradigm shift from traditional high-end servers to "edge" devices of limited processing and memory capabilities. These devices, together with sensors, regularly produce very high data volumes nowadays. For m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8bebb4426d6e952fd07c5c89da127d1b
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030192730
ICWE
ICWE
A growing list of domains, in the forefront of which are Web data and applications, are modeled by graph representations. In content-driven graph analytics, knowledge must be extracted from large numbers of available data graphs. As the number of dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0ddfef48563eb46207c2dc13c4e45e68
https://doi.org/10.1007/978-3-030-19274-7_9
https://doi.org/10.1007/978-3-030-19274-7_9
Autor:
Katerina Doka, Nikolaos Papailiou, Victor Giannakouris, Nectarios Koziris, Dimitrios Tsoumakos, Ioannis Mytilinis
Publikováno v:
Real-Time Business Intelligence and Analytics ISBN: 9783030241230
BIRTE (Revised Selected Papers)
BIRTE (Revised Selected Papers)
We present IReS, the Intelligent Resource Scheduler that is able to abstractly describe, optimize and execute any batch analytics workflow with respect to a multi-objective policy. Relying on cost and performance models of the required tasks over the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a593790167713b0c997d6b09454b08f9
https://doi.org/10.1007/978-3-030-24124-7_9
https://doi.org/10.1007/978-3-030-24124-7_9
Autor:
Kostas Tsichlas, Andreas Kosmatopoulos, Dimitrios Tsoumakos, Katerina Doka, Apostolos N. Papadopoulos, Spyros Sioutas
Publikováno v:
Information Processing Letters. 162:105990
The skyline of a set P of points consists of the “best” points with respect to minimization or maximization of the attribute values. A point p dominates another point q if p is as good as q in all dimensions and it is strictly better than q in at
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4012edbda5697969bdd6c159923ca7d0
https://doi.org/10.1016/j.ipl.2020.105990
https://doi.org/10.1016/j.ipl.2020.105990
Publikováno v:
CIKM
The plethora of publicly available data sources has given birth to a wealth of new needs and opportunities. The ever increasing amount of data has shifted the analysts' attention from optimizing the operators for specific business cases, to focusing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd007207c9521209fb97bcc9eaa81c1f
https://doi.org/10.1145/3269206.3271731
https://doi.org/10.1145/3269206.3271731
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 7, Iss 1, Pp 1-20 (2018)
Application deployment is a crucial operation for modern cloud providers. The ability to dynamically allocate resources and deploy a new application instance based on a user-provided description in a fully automated manner is of great importance for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7815b85f89926e54f6fef8b115c9c78f
https://doi.org/10.1186/s13677-018-0112-9
https://doi.org/10.1186/s13677-018-0112-9